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644 results for “data visualization”
Data for: Susceptibility of domain experts to color manipula-tion indicate a need for design principles in data visualization (PLoS one)
<p>This data set accompanies the paper "<strong>Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization</strong>" by Markus Christen, Peter Brugger<span> </span>and Sara Irina Fabrikant, revision submitted to PLoS One. The paper will be open access, further information will be available there.</p>
Data Visualization of Weight Sensor and Event Detection of Aifi Store
<p><a href="https://www.aifi.com/">Aifi</a> Store is an autonomus store for cashier-less shopping experience which is achieved by multi modal sensing (Vision modality, weight modality and location modality). Aifi Nano store layout (Fig 1) (Image Credits: <a href="https://dl.acm.org/doi/10.1145/3360322.3361018">AIM3S</a> research paper).</p> <p><strong>Overview:</strong><br> The store is organized in the gondola's and each gondola has shelfs that holds the products and each shelf has weight sensor plates. These weight sensor plates data is used to find the event trigger (pick up, put down or no event) from which we can find the weight of the product picked.</p> <p>Gondola is similar to vertical fixture consisting of horizontal shelfs in any normal store and in this case there are 5 to 6 shelfs in a Gondola. Every shelf again is composed of weight sensing plates, weight sensing modalities, there are around 12 plates on each shelf.</p> <p>Every plate has a sampling rate of **60Hz**, so there are 60 samples collected every second from each plate</p> <p>The pick up event on the plate can be observed and marked when the weight sensor reading decreases with time and increases with time when the put down event happens.</p> <p><strong>Event Detection:</strong></p> <p>The event is said to be detected if the moving variance calculated from the raw weight sensor reading exceeds a set threshold of (10000gm^2 or 0.01kg^2) over the sliding window length of 0.5 seconds, which is half of the sampling rate of sensors (i.e 1 second).</p> <p>There are 3 types of events:</p> <ol> <li>Pick Up Event (Fig 2)= Object being taken from the particular gondola and shelf from the customer</li> <li>Put Down Event (Fig 3)= Object being placed back from the customer on that particular gondola and shelf</li> <li>No Event = (Fig 4)No object being picked up from that shelf</li> </ol> <p><strong>NOTE:</strong></p> <ol> <li>1.The python script must be in the same folder as of the <em>weight.csv</em> files and .<em>csv</em> files should not be placed in other subdirectories.</li> <li>2.The videos for the corresponding weight sensor data can be found in the <strong>"Videos folder"</strong> in the repository and are named similar to their corresponding <strong>".csv"</strong> files.</li> <li>3.Each video files consists of video data from 13 different camera angles.</li> </ol> <p><strong>Details of the weight sensor files:</strong></p> <p>These weight.csv (Baseline cases and team particular cases ) files are from the AIFI CPS IoT 2020 week.There are over 50 cases in total and each file has 5 columns (Fig 5) (timestamp, reading (in grams), gondola, shelf, plate number).</p> <p>Each of these files have data of around 2-5 minutes or 120 seconds in the form of timestamp. In order to unpack date and time from timestamp use <em>datetime</em> module from python.</p> <p><strong>Details of the <em>product.csv</em> files:</strong></p> <p>There are <em>product.csv</em> files for each test cases and these files provide the detailed information about the product name, product location (gondola number, shelf number and plate number) in the store, product weight(in grams), also link to the image of the product.</p> <p><strong>Instruction to run the script:</strong></p> <p>To start analysing the weigh.csv files using the python script and plot the timeseries plot for corresponding files.</p> <ol> <li>Download the dataset.</li> <li>Make sure to place the python/ jupyter notebook file is in same directory as the .csv files.</li> <li>Install the requirements<br> <code>$ pip3 install -r requirements.txt</code></li> <li>Run the python script Plot.py<br> <code>$ python3 Plot.py</code></li> </ol> <p>After the script has run successfully you will find the corresponding folders of weight.csv files which contain the figures (weight vs timestamp) in the format</p> <p><strong>Instruction to run the Jupyter Notebook:</strong></p> <p>Run the Plot.ipynb file using Jupyter Notebook by placing .csv files in the same directory as the Plot.ipynb script.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> gondola_number,shelf_number.png</p> <p> Ex: 1,1.png (Fig 4) (<em>Timeseries Graph</em>)</p>
Actionable Data Visualizations
<p>1<sup>st</sup> Lecture</p>
The visualization data for script at https://github.com/BinWang0213/PMV_Challenge2020
<p>The visualization data file used for the script at https://github.com/BinWang0213/PMV_Challenge2020.</p> <p>The files generated based on the data published at https://www.digitalrocksportal.org/projects/175 (10.17612/HDP8-0149)</p> <p> </p>
Data from: Nest structure affects auditory and visual detectability, but not predation risk, in a tropical songbird community
1. Offspring mortality varies dramatically among species with critical demographic and evolutionary ramifications, yet the causes of this variation remain unclear. Nests are widely used for breeding across taxa and thought to influence offspring mortality risk. Traditionally, more complex, enclosed nest structures are thought to reduce offspring predation by reducing the visibility of nest contents and muffling offspring sounds compared to open nests. Direct tests of the functional bases for nest structure influences on predation risk are lacking. 2. We used experiments and 10 years of observational data to examine how nest structure influences nest predation risk in a diverse community of tropical songbirds. First, we examined how nest size was related to nest structure and nest predation rates across species. Second, we assessed how nest structure influences the detectability of nestling begging calls both in field and laboratory settings. Finally, we examined how the acoustic properties of different nest structures influence nest predation risk. Specifically, we experimentally broadcast begging calls from open and enclosed nests to determine how auditory cues and nest structure interact to affect predation on plasticine and quail eggs. We also tested whether nest structure was associated with differences in nest predation rates between the incubation (no begging cues) and nestling (begging cues) stages. 3. We found that enclosed nests are larger than open nests after accounting for adult size, and larger nests had increased predation rates. Moreover, enclosed nests did not consistently alter nestling begging calls in ways that reduce the likelihood of predation compared to open nests. Indeed, begging cues increased predation rates for enclosed but not open cup nests in our playback experiment, and nest predation rates showed greater increases after hatching in enclosed than open cup nests. 4. Ultimately, enclosed nests do not necessarily provide greater predation benefits than open nests in contrast to long standing theory.
Data from: Retinotopic-like maps of spatial sound in primary 'visual' cortex of blind human echolocators
The functional specialisations of cortical sensory areas were traditionally viewed as being tied to specific modalities. A radically different emerging view is that the brain is organized by task rather than sensory modality, but it has not yet been shown that this applies to primary sensory cortices. Here we report such evidence by showing that primary 'visual' cortex can be adapted to map spatial locations of sound in blind humans who regularly perceive space through sound echoes. Specifically, we objectively quantify the similarity between measured stimulus maps for sound eccentricity and predicted stimulus maps for visual eccentricity in primary 'visual' cortex (using a probabilistic atlas based on cortical anatomy) to find that stimulus maps for sound in expert echolocators are directly comparable to those for vision in sighted people. Furthermore, the degree of this similarity is positively related with echolocation ability. We also rule out explanations based on top-down modulation of brain activity – e.g. through imagery. This result is clear evidence that task-specific organization can extend even to primary sensory cortices, and in this way is pivotal in our reinterpretation of the functional organisation of the human brain.
Data from: Examining the microclimate hypothesis in Amazonian birds: indirect tests of the 'visual constraints' mechanism
Proposed mechanisms for the decline of terrestrial and understory insectivorous birds in the tropics include a related subset that together has been termed the "microclimate hypothesis." One prediction from this hypothesis is that sensitivity to bright light environments discourages birds of the dimly lit rainforest interior from using edges, gaps, or disturbed forest. Using a hierarchical Bayesian framework and capture data across time and space, we tested this by first determining vulnerability based on differences in within-species capture rates between disturbed and undisturbed forest for 64 bird species at the Biological Dynamics of Forest Fragments Project in central Amazonian Brazil. We found that 35 species (55%) were vulnerable to anthropogenic habitat degradation, whereas only four (6%) were more commonly captured in degraded forest. To infer visual sensitivity, we then examined two different characters: eye size (maximum pupil diameter) relative to body mass and the initiation time of dawn song, which presumably reflects a species' visual capacity under low light intensities. We predicted that species with large relative eye sizes and birds with earlier dawn songs would exhibit increased vulnerability in degraded habitats with bright light. Contrary to our predictions, however, vulnerability was positively correlated with the mean start time of dawn song. This indicates that species that wait to initiate dawn song are also more vulnerable to habitat degradation. After correcting for body size, there was no effect of eye size on vulnerability. Together, our results do not provide quantitative support for the light sensitivity mechanism of the microclimate hypothesis. More sensitive, experimental tests, such as behavioral assays with controlled light environments, especially in a comparative framework, are needed to rigorously evaluate the role of light sensitivity as an aspect of the microclimate hypothesis among Neotropical birds.
Experimental Data for: Comparing Trace Visualizations for Program Comprehension through Controlled Experiments
<p>For efficient and effective program comprehension, it is essential to provide software engineers with appropriate visualizations of the program's execution traces. Empirical studies, such as controlled experiments, are required to assess the effectiveness and efficiency of proposed visualization techniques.</p> <p>We present controlled experiments to compare the trace visualization tools EXTRAVIS and ExplorViz in typical program comprehension tasks. We replicate the first controlled experiment with a second one targeting a differently sized software system. In addition to a thorough analysis of the strategies chosen by the participants, we report on common challenges comparing trace visualization techniques. Besides our own replication of the first experiment, we provide a package containing all our experimental data to facilitate the verifiability, reproducibility and further extensibility of our presented results.</p> <p>Although subjects spent similar time on program comprehension tasks with both tools for a small-sized system, analyzing a larger software system resulted in a significant efficiency advantage of 28 percent less time spent by using ExplorViz. Concerning the effectiveness (correct solutions for program comprehension tasks), we observed a significant improvement of correctness for both object system sizes of 39 and 61 percent with ExplorViz.</p> <p>This package contains the experimental data.</p>
Experimental Data for: Hierarchical Software Landscape Visualization for System Comprehension: A Controlled Experiment
<p>In many enterprises the number of deployed applications is constantly increasing. Those applications - often several hundreds - form large software landscapes. The comprehension of such landscapes is frequently impeded due to, for instance, architectural erosion, personnel turnover, or changing requirements. Therefore, an efficient and effective way to comprehend such software landscapes is required. The current state of the art often visualizes software landscapes via flat graph-based representations of nodes, applications, and their communication.</p> <p>In our ExplorViz visualization, we introduce hierarchical abstractions aiming at solving typical system comprehension tasks fast and accurately for large software landscapes. To evaluate our hierarchical approach, we conduct a controlled experiment comparing our hierarchical landscape visualization to a flat, state-of-the-art visualization. In addition, we thoroughly analyze the strategies employed by the participants and provide a package containing all our experimental data to facilitate the verifiability, reproducibility, and further extensibility of our results.</p> <p>We observed a statistically significant increase of 14 % in task correctness of the hierarchical visualization group compared to the flat visualization group in our experiment. The time spent on the system comprehension tasks did not show any significant differences. The results backup our claim that our hierarchical concept enhances the current state of the art in landscape visualization.</p> <p>This package contains our experimental data.</p>
Data from The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration
<p>Landscape data for "The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration", G. Ochoa, N. Veerapen, D. Whitley and E. Burke. Artificial Evolution (EA 2015), 26-28 October 2015, Lyon, France.</p>
DNA Data Visualizations Generated with DDV Software
<p>A ZIP file containing the DNA Data Visualization interfaces generated with DDV software. These interfaces are available live at: at: http://www.photomedia.ca/DDV/dnadata/ </p> <p>The corresponding source code for this project is at: https://github.com/photomedia/DDV</p> <p> </p>
Supplementary material 17: Generating Interactive Dashboard Charts Based on Plazi Treatment Data from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
A brief explanation of how to create and customize interactive charts using data on Plazi.
Representations of language in a model of visually grounded speech signal: Data
<p>The set of datafiles to reproduce results from:</p> <ul> <li>Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. ACL. arXiv preprint: https://arxiv.org/abs/1702.01991</li> </ul>
Opsin data from: Multiple axes of visual system diversity in Ithomiini, an ecologically diverse tribe of mimetic butterflies
<p><span>The striking structural variation seen in arthropod visual systems can be explained by the overall quantity and spatio-temporal structure of light within habitats coupled with developmental and physiological constraints. However, little is currently known about how fine-scale variation in visual structures arise across shorter evolutionary and ecological scales. In this study, we characterise patterns of interspecific (between species), intraspecific (between sexes) and intraindividual (between eye regions) variation in the visual system of four ithomiine butterfly species. These species are part of a diverse 26-Myr-old Neotropical radiation where changes in mimetic colouration are associated with fine-scale shifts in ecology, such as microhabitat preference. By using a combination of selection analyses on visual opsin sequences, in-vivo ophthalmoscopy, micro-computed tomography (micro-CT), immunohistochemistry, confocal microscopy, and neural tracing, we quantify and describe physiological, anatomical, and molecular traits involved in visual processing. Using these data, we provide evidence of substantial variation within the visual systems of Ithomiini, including: i) relaxed selection on visual opsins, perhaps mediated by habitat preference, ii) interspecific shifts in visual system physiology and anatomy, and iii) extensive sexual dimorphism, including the complete absence of a butterfly-specific optic neuropil in the males of some species. We conclude that considerable visual system variation can exist within diverse insect radiations, hinting at the evolutionary lability of these systems to rapidly develop specialisations to distinct visual ecologies, with selection acting at both the perceptual, processing, and molecular level.</span></p>
STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer
<p>Scripts generating figures of the paper titled "STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer".</p>
Data, stimuli, and analyses for "High-level aftereffects reveal the role of statistical features in visual shape encoding"
<h2><strong>Data and code share.</strong></h2><p>This record contains data and code (written in MATLAB) to reproduce the results shown in:</p><p>Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (<i>in press</i>) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology</p><p>Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.</p><h3><strong>Figure 1</strong></h3><ul><li><i>Fig1C_shapespace.m</i>: draw shape space (as in Figure 1C)</li><li><i>Fig1EFG_plotpsychometricdata.m</i>: fit psychometric model to pooled data and plot (as in Figure 1EFG)</li><li><i>getExptShapesHbias.m</i>: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see <i>fitGabPyr2Hbais.m</i> or <i>fitTAEGANfit2Hbais.m</i>)</li></ul><h3><strong>Figure 2 and 3A</strong></h3><ul><li><i>fig3A_modeval_expt1.m</i>: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).</li></ul><p>Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:</p><h4>Model: <strong>GabPyrAE</strong> </h4><ul><li><i><strong>note: </strong></i>To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory</li><li><i>fig2B_GabPyrAEVisFigs.m</i>: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )</li><li><i>figS2BC_GabPyrAEExp</i>.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.</li><li><i>getGabPyrAEMod.m: </i>given an adaptor and test image, this function produces the unfit GabPyrAE prediction</li><li><i>fitGabPyr2Hbias</i>.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitGabPyrNormMod2Stims</i>.m</li><li><i>evalGabPyrAE_fitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalGabPyrAE_unfitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>TAE</strong></h4><ul><li><i>fig2CD_TAEmod.m</i>: produce TAE model images for example adaptor and test image (as in Figure 2CD)</li><li><i>getTAEModonShape.m: </i>given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.</li><li><i>getTAEGAN_spwt_onShape.m: </i>given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getTAE_spwt_onShape_nn.m: </i>given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitTAEGAN2Hbias</i>.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAEGANMod2Stims</i>.m</li><li><i>fitTAENN2Hbias</i>.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAENNMod2Stims</i>.m.</li><li><i>evalTAEGAN_fitmod_aic.m</i>: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAENN_fitmod_aic.m</i>: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAE_unfitmod_aic.m</i>: evaluate TAE Original model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>PSAE</strong></h4><ul><li><i>fig2EF_PSAEmod.m</i>: produce PSAE model images for example adaptor and test image</li><li><i>getPos_spwt_ShiftononShape_io.m: </i>given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getPos_spwt_ShiftonShape_io_nn.m: </i>given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitPSAEGAN2Hbias</i>.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAEGANMod2Stims</i>.m</li><li><i>fitPSAENN2Hbias</i>.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAENNMod2Stims</i>.m.</li><li><i>evalPSAEGAN_fitmod_aic.m</i>: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalPSAENN_fitmod_aic.m</i>: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li></ul><h4>Model: <strong>ShapeComp </strong>and <strong>No Adaptation</strong></h4><ul><li><i>eval_ShapeComp _aic.m</i>: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.</li><li><i>eval_NoAdaptation _aic.m</i>: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.</li></ul><h3><strong>Figure 3BC</strong></h3><ul><li><i>fig3BC_Experiment2.m</i>: load, analyze, and plot experiment 2 data (as in Figure 3B and C).</li><li><i>figS4_Expt2_stimuli.m</i>: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)</li></ul><p> </p>
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
Data from: Dietary partitioning among three cryptobentic reef fish mesopredators revealed by visual analysis, metabarcoding of gut content, and stable isotope analysis
<p>Understanding how mesopredators partition their diet and the identity of consumed prey can assist in understanding the ecological role predators and prey play in ecosystem trophodynamics. Here, we assessed the diet of three common coral reef mesopredators; <em>Pseudochromis flavivertex</em>, <em>Pseudochromis fridmani</em>, and <em>Pseudochromis olivaceus</em> from the family Pseudochromidae, commonly known as dottybacks, using a combination of i) visual stomach content analysis, ii) stomach content DNA metabarcoding (18S, COI), and iii) stable isotope analysis (δ<sup>15</sup>N, δ<sup>13</sup>C). In addition, <em>P. flavivertex</em> is found in two distinct color morphs in the Red Sea, providing an opportunity to analyze intra-morph differences. These techniques revealed partitioning in the dietary composition and resource use among species. Arthropods comprised the main dietary component of <em>P. flavivertex</em> (18S > 60%; COI > 10%), and <em>P. olivaceus</em> (18S = 57.2%) while <em>P. fridmani</em> ingested predominantly mollusks (18S = 51.3%, COI = 24.6%). Despite being small predators, microplastics were found in the gut content of some of these fishes. Stable isotope analysis showed differences in species' isotopic niche breadth and trophic position. <em>Pseudochromis olivaceus</em> presented the largest isotopic niche (SEA<sub>C</sub> = 1.61‰<sup>2</sup>), while <em>P. fridmani</em> showed the smallest isotopic niche (SEA<sub>C</sub> = 0.45‰<sup>2</sup>) among species. Although the two techniques used for stomach content analysis did not show differences in the diet within color morphs of <em>P. flavivertex</em>, they differed in the isotopic niche and resource use. Despite our limited sampling, our findings provide evidence of species-specific differences in the trophic ecology of dottybacks and demonstrate their important role as predators of cryptic invertebrates and small fishes. This study highlights the importance of combining several approaches (short-term: visual analysis and DNA metabarcoding; and long-term: isotope analysis) when assessing the feeding habits of coral reef fish, as they provide complementary information necessary to delimit their niches and understand the role that small mesopredators play in coral reef ecosystems.</p>
Data from: Diversity and molecular evolution of non-visual opsin genes across environmental, developmental, and morphological adaptations in frogs
<p>Dataset for the article Diversity and molecular evolution of non-visual opsin genes across environmental, developmental, and morphological adaptations in frogs. Includes non-visual opsin coding sequences from frogs, sequence alingments, phylogenetics trees, and raw PAML results files.</p>
Source Data: Visualization of chromosomal reorganization induced by heterologous fusions in the mammalian nucleus
<p>Source data from Visualization of chromosomal reorganization induced by heterologous fusions in the mammalian nucleus</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.